Candidate: Benjamin Arnold I am an epidemiologist at the University of California, Berkeley. I completed my MA in Biostatistics and a PhD in Epidemiology from UC Berkeley in 2009. Since then, I have worked as an epidemiologist in Professor Jack Colford's group. The opportunity to work as the coordinating epidemiologist for a touchstone, multi-country cluster randomized trial - combined with the addition of two children to my family - led me to delay my academic career. I am now ready to restart my career progress toward independent investigator status. My long-term career goal is to become a leader in the application of novel statistical methods to target and evaluate interventions that reduce the burden of enteric infections and neglected tropical diseases (NTDs) in low-income countries. This research focus and career objective build from my experience and from a growing collaboration with Dr. Patrick Lammie at the US Centers for Disease Control (CDC) that started in 2013 and has introduced me to seroepidemiologic research. My background in epidemiologic methods, biostatistics, and international field research makes me uniquely qualified to make significant contributions to infectious disease epidemiology at the interface between recent advances in statistical methodology and serological assays. Environment: University of California, Berkeley To achieve my career goal, I have developed a training and mentoring plan that focuses on recent advances in statistics (semi-parametric estimation theory and machine learning) and on infectious disease immunology. These are two areas where additional training will open up significant and unique opportunities for me to make meaningful contributions to seroepidemiologic research, and will enable me to launch an independent career as a productive faculty member at UC Berkeley. I have assembled a multidisciplinary mentoring team of senior investigators in biostatistics and immunology to support my training, research, and career objectives. Mark van der Laan (primary mentor, biostatistics) will guide my training in semi-parametric methods and machine learning. Alan Hubbard (co-mentor, biostatistics) will guide my translation of the methodology to applications for enteric pathogens and NTDs. Patrick Lammie (co-mentor at CDC, immunology) will guide my immunology training and research with his expertise in the immunology of enteric pathogens and NTDs Research: New Serological Measures of Infectious Disease Transmission Background: Recent advances in multiplex antigen assays have led to the development of low-cost and sensitive methods to measure enteric pathogens and neglected tropical diseases (NTDs). There have not been commensurate advances in the statistical methods used to derive measures of transmission intensity from antibody response. Translating antibody response into metrics of transmission intensity is a key step from a public health perspective because it enables us to target intervention programs to the populations most in need and then measure the effectiveness of those programs. Aims and Methods: The overarching goal of this research is to develop a methodologic framework to translate antibody response measured in cross-sectional surveys into measures of transmission intensity for enteric pathogens (7 included in the study, e.g., Cryptosporidium parvum, enterotoxigenic E. coli) and neglected tropical diseases (principal focus: lymphatic filariasis). We approach this goal from two novel perspectives. In Aim 1, we draw on the peak shift phenomenon for infectious diseases, and hypothesize that changes in transmission will be detectable in the age-specific antibody response curve. At lower transmission, antibody levels should decline across all ages due to fewer and less frequent active infections, leading to an overall shift in the age-specific response curve. We will evaluate the approach by comparing antibody response curves for young children with different exposures (improved vs. unimproved drinking water for enteric pathogens; pre- versus post- mass drug administration for lymphatic filariasis) in large, well characterized cohorts in Kenya, Tanzania, and Haiti. In Aim 2, we will develop semi-parametric methods to estimate the force of infection (seroconversion rate) from seroprevalence data for pathogens where seroreversion is possible, using lymphatic filariasis as an example. Our new approach marks a significant advance over previous work in this area by making few modeling assumptions and by allowing for the flexible control of confounding between comparison groups. We will evaluate the approach in Haiti by measuring the effect of mass drug administration on the force of infection for lymphatic filariasis For all of the methods, we will create user-friendly, open source software to accelerate translation to applied research. The Future: This mentored training and research plan represents a natural next step for me on a productive and collaborative path to independence at UC Berkeley. It will set the stage for a broader R01-level research portfolio that applies the newly developed methods to primary research studies that evaluate the impact of interventions on enteric infections, and help target and monitor global elimination efforts for NTDs.